There is a history of Decision Making Under Uncertainty* as a distinct
discipline going back to Pascal and his wager and Bernoulli and his
Expected Utility vs Expected Value. It is now taught as the heart of
Decision Theory and as a cousin of Game Theory.
From Disciplined Systematic Global Macro Views:
A good decision rule to follow is that there are no simple rules for complex problems, but we can learn from others on how to improve our decision-making.
Jeff Bezos of Amazon fame has diligently worked on the Amazon decision-making process. As determined by interviews and his shareholder letters, it can be boiled down to two key rules:
1. Make a decision if you have 70% of the information you think you need. You will never have all of the information you think you need so just accept it. Find an acceptable information level of what you think is necessary to make a decision and then act. This makes perfect sense in an uncertainty world. We will never have all the facts. A corollary rule is to "disagree and commit". You don't have to get 100% agreement on a decision but the organization has to commit to action.
2. Get comfortable with uncertainty through flexibility. Don't fret about decisions that can be reversed. Have an exit strategy in case something goes wrongs. Allow for flexibility because you may need it. Make sure you know what decisions can be easily reversed. Don't avoid making decisions. Focus on fixing it, if it is wrong....MORE
HT: Alpha Ideas
Related:
Ogilvy's Rory Sutherland Has Some Thoughts
"Poker and the Psychology of Uncertainty"
That second post has many many links, including:
Paul Tudor Jones On 'Imperfect Information'
"How to Choose With Less Than Perfect Information"
Following up on yesterday's "Paul Tudor Jones On 'Imperfect Information'", a repost from 2014:
Less-than-perfect-information is a term from decision theory.
"Poker, Speeding Tickets, and Expected Value: Making Decisions in an Uncertain World"
And many more. Use the 'Search Blog' box if interested.
